Skip to content

Latest commit

 

History

History
32 lines (20 loc) · 3.4 KB

sequence-to-sequence architecture.md

File metadata and controls

32 lines (20 loc) · 3.4 KB

#highlevel

Seq2Seq model or Sequence-to-Sequence model, is a machine learning architecture designed for tasks involving sequential data. It takes an input sequence, processes it, and generates an output sequence. The architecture consists of two fundamental components: an encoder and a decoder. Seq2Seq models have significantly improved the quality of machine translation systems making them an important technology. The article aims to explore the fundamentals of the seq2seq model and its applications along with its advantages and disadvantages.

What is Seq2Seq model?

The seq2Seq model is a kind of machine learning model that takes sequential data as input and generates also sequential data as output. Before the arrival of Seq2Seq models, the machine translation systems relied on statistical methods and phrase-based approaches. The most popular approach was the use of phrase-based statistical machine translation (SMT) systems. That was not able to handle long-distance dependencies and capture global context.

**Advantages of seq2seq Models

  • Flexibility: Seq2Seq models can handle a wide range of tasks such as machine translation, text summarization, and image captioning, as well as variable-length input and output sequences.
  • Handling Sequential Data: Seq2Seq models are well-suited for tasks that involve sequential data such as natural language, speech, and time series data.
  • Handling Context: The encoder-decoder architecture of Seq2Seq models allows the model to capture the context of the input sequence and use it to generate the output sequence.
  • Attention Mechanism: Using attention mechanisms allows the model to focus on specific parts of the input sequence when generating the output, which can improve performance for long input sequences.

Disadvantages of seq2seq Models

  • Computationally Expensive: Seq2Seq models require significant computational resources to train and can be difficult to optimize.
  • Limited Interpretability: The internal workings of Seq2Seq models can be difficult to interpret, which can make it challenging to understand why the model is making certain decisions.
  • Overfitting: Seq2Seq models can overfit the training data if they are not properly regularized, which can lead to poor performance on new data.
  • Handling Rare Words: Seq2Seq models can have difficulty handling rare words that are not present in the training data.
  • Handling Long input Sequences: Seq2Seq models can have difficulty handling input sequences that are very long, as the context vector may not be able to capture all the information in the input sequence.

Applications of Seq2Seq model

Throughout the article, we have discovered the machine translation is the real-world application of seq2seq model. Let’s explore more applications:

  • Text Summarization: The seq2seq model effectively understands the input text which makes it suitable for news and document summarization.
  • Speech Recognition: Seq2Seq model, especially those with attention mechanisms, excel in processing audio waveform for ASR. They are able to capture spoken language patterns effectively.
  • Image Captioning: The seq2seq model integrate image features from CNNs with textual generation capabilities for image captioning. They are capable to describe images in a human readable format.